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Deep Learning Masterclass With Tensorflow 2 Over 20 Projects
Last updated 2/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 45.88 GB | Duration: 102h 36m
Master Deep Learning with TensorFlow 2 with Computer Vision,Natural Language Processing, Sound Recognition & Deployment
What you'll learn
The Basics of Tensors and Variables with Tensorflow
Basics of Tensorflow and training neural networks with TensorFlow 2.
Convolutional Neural Networks applied to Malaria Detection
Building more advanced Tensorflow models with Functional API, Model Subclassing and Custom Layers
Evaluating Classification Models using different metrics like: Precision,Recall,Accuracy and F1-score
Classification Model Evaluation with Confusion Matrix and ROC Curve
Tensorflow Callbacks, Learning Rate Scheduling and Model Check-pointing
Mitigating Overfitting and Underfitting with Dropout, Regularization, Data augmentation
Data augmentation with TensorFlow using TensorFlow image and Keras Layers
Advanced augmentation strategies like Cutmix and Mixup
Data augmentation with Albumentations with TensorFlow 2 and PyTorch
Custom Loss and Metrics in TensorFlow 2
Eager and Graph Modes in TensorFlow 2
Custom Training Loops in TensorFlow 2
Integrating Tensorboard with TensorFlow 2 for data logging, viewing model graphs, hyperparameter tuning and profiling
Machine Learning Operations (MLOps) with Weights and Biases
Experiment tracking with Wandb
Hyperparameter tuning with Wandb
Dataset versioning with Wandb
Model versioning with Wandb
Human emotions detection
Modern convolutional neural networks(Alexnet, Vggnet, Resnet, Mobilenet, EfficientNet)
Transfer learning
Visualizing convnet intermediate layers
Grad-cam method
Model ensembling and class imbalance
Transformers in Vision
Model deployment
Conversion from tensorflow to Onnx Model
Quantization Aware training
Building API with Fastapi
Deploying API to the Cloud
Object detection from scratch with YOLO
Image Segmentation from scratch with UNET model
People Counting from scratch with Csrnet
Digit generation with Variational autoencoders (VAE)
Face generation with Generative adversarial neural networks (GAN)
Sentiment Analysis with Recurrent neural networks, Attention Models and Transformers from scratch
Neural Machine Translation with Recurrent neural networks, Attention Models and Transformers from scratch
Intent Classification with Deberta in Huggingface transformers
Neural Machine Translation with T5 in Huggingface transformers
Extractive Question Answering with Longformer in Huggingface transformers
E-commerce search engine with Sentence transformers
Lyrics Generator with GPT2 in Huggingface transformers
Grammatical Error Correction with T5 in Huggingface transformers
Elon Musk Bot with BlenderBot in Huggingface transformers
Requirements
Basic Math
Access to an internet connection, as we shall be using Google Colab (free version)
Basic Knowledge of Python
Description
Deep Learning is one of the most popular fields in computer science today. It has applications in many and very varied domains. With the publishing of much more efficient deep learning models in the early 2010s, we have seen a great improvement in the state of the art in domains like Computer Vision, Natural Language Processing, Image Generation, and Signal Processing. The demand for Deep Learning engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn't easy. There's so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, and built by Google) and Huggingface. We shall start by understanding how to build very simple models (like Linear regression models for car price prediction, text classifiers for movie reviews, binary classifiers for malaria prediction) using Tensorflow and Huggingface transformers, to more advanced models (like object detection models with YOLO, lyrics generator model with GPT2 and Image generation with GANs)After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep-learning solutions that big tech companies encounter.You will learn:The Basics of Tensorflow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision TransformersEvaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)Mitigating overfitting with Data augmentationAdvanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, TensorboardMachine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)Binary Classification with Malaria detection Multi-class Classification with Human Emotions DetectionTransfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet) and Vision Transformers (VITs)Object Detection with YOLO (You Only Look Once)Image Segmentation with UNetPeople Counting with Csrnet Model Deployment (Distillation, Onnx format, Quantization, Fastapi, Heroku Cloud)Digit generation with Variational AutoencodersFace generation with Generative Adversarial Neural NetworksText Preprocessing for Natural Language Processing.Deep Learning algorithms like Recurrent Neural Networks, Attention Models, Transformers, and Convolutional neural networks.Sentiment analysis with RNNs, Transformers, and Huggingface Transformers (Deberta)Transfer learning with Word2vec and modern Transformers (GPT, Bert, ULmfit, Deberta, T5.)Machine translation with RNNs, attention, transformers, and Huggingface Transformers (T5)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)Intent Classification with Deberta in Huggingface transformersNamed Entity Relation with Roberta in Huggingface transformersNeural Machine Translation with T5 in Huggingface transformersExtractive Question Answering with Longformer in Huggingface transformersE-commerce search engine with Sentence transformersLyrics Generator with GPT2 in Huggingface transformersGrammatical Error Correction with T5 in Huggingface transformersElon Musk Bot with BlenderBot in Huggingface transformersSpeech recognition with RNNsIf you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.Enjoy!!!
Overview
Section 1: Introduction
Lecture 1 Welcome
Lecture 2 General Introduction
Lecture 3 Link to Code
Section 2: Tensors and Variables
Lecture 4 Tensor Basics
Lecture 5 Tensor Initialization and Casting
Lecture 6 Indexing
Lecture 7 Maths Operations in Tensorflow
Lecture 8 Linear Algebra Operations in Tensorflow
Lecture 9 Ragged Tensors
Lecture 10 Sparse Tensors
Lecture 11 String Tensors
Lecture 12 Tensorflow Variables
Section 3: Building a Simple Neural Network in Tensorflow
Lecture 13 Task Understanding
Lecture 14 Data Preparation
Lecture 15 Linear Regression Model
Lecture 16 Error sanctioning
Lecture 17 Training and Optimization
Lecture 18 Performance Measurement
Lecture 19 Validation and testing
Lecture 20 Corrective Measures
Section 4: Building Convolutional Neural Networks [Malaria Diagnosis]
Lecture 21 Task understanding
Lecture 22 Data Preparation
Lecture 23 Data visualization
Lecture 24 Data Processing
Lecture 25 How and Why Convolutional Neural Networks work
Lecture 26 Building Convnets in Tensorflow
Lecture 27 Binary Crossentropy loss
Lecture 28 Convnet training
Lecture 29 Model evaluation and testing
Lecture 30 Loading and Saving Tensorflow Models to Google Drive
Section 5: Building more advanced Models with Functional API, Subclassing and Custom Layers
Lecture 31 Functional API
Lecture 32 Model Subclassing
Lecture 33 Custom Layers
Section 6: Evaluating Classification Models
Lecture 34 Precision,Recall and Accuracy
Lecture 35 Confusion Matrix
Lecture 36 ROC Plots
Section 7: Improving Model Performance
Lecture 37 Tensorflow Callbacks
Lecture 38 Learning rate scheduling
Lecture 39 Model checkpointing
Lecture 40 Mitigating overfitting and underfitting
Section 8: Data augmentation
Lecture 41 Data augmentation with TensorFlow using tf.image and Keras Layers
Lecture 42 Mixup Data augmentation with TensorFlow 2 with intergration in tf.data
Lecture 43 Cutmix Data augmentation with TensorFlow 2 and intergration in tf.data
Lecture 44 Albumentations with TensorFlow 2 and PyTorch for Data augmentation
Section 9: Advanced Tensorflow Concepts
Lecture 45 Custom Loss and Metrics
Lecture 46 Eager and graph modes
Lecture 47 Custom training loops
Section 10: Tensorboard integration
Lecture 48 Data logging
Lecture 49 Viewing model graphs
Lecture 50 Hyperparameter tuning
Lecture 51 Profiling and other visualizations with Tensorboard.
Section 11: MLOps with Weights and Biases
Lecture 52 Experiment tracking
Lecture 53 Hyperparameter tuning with wandb
Lecture 54 Dataset Versioning with Weights and Biases and TensorFlow 2
Lecture 55 Model Versioning with Weights and Biases and TensorFlow 2
Section 12: Human Emotions Detection
Lecture 56 Data preparation
Lecture 57 Modeling and training
Lecture 58 Data augmentation
Lecture 59 Tensorflow records
Section 13: Modern Convolutional Neural Networks
Lecture 60 Alexnet
Lecture 61 Vggnet
Lecture 62 Resnet
Lecture 63 Coding Resnets
Lecture 64 Mobilenet
Lecture 65 Efficientnet
Section 14: Transfer Learning
Lecture 66 Leveraging pretrained models
Lecture 67 Finetuning
Section 15: Understanding the blackbox
Lecture 68 Visualizing intermediate layers
Lecture 69 Grad-cam method
Section 16: Ensembling and class imbalance
Lecture 70 Ensembling
Lecture 71 Class Imbalance
Section 17: Transformers in Vision
Lecture 72 Understanding VITs
Lecture 73 Building VITs from scratch
Lecture 74 Finetuning Huggingface transformers
Lecture 75 Model evaluation with wandb
Lecture 76 Data efficient transformers
Lecture 77 Swin transformers
Section 18: Model deployment
Lecture 78 Model Conversion from Tensorflow to Onnx
Lecture 79 Understanding quantization
Lecture 80 Practical quantization of Onnx
Lecture 81 Quantization aware training
Lecture 82 Conversion to Tensorflow Lite
Lecture 83 What is an API
Lecture 84 Building the Emotions Detection API with Fastapi
Lecture 85 Deploy the Emotions Detection API to the Cloud
Lecture 86 Load tesing the Emotions Detection API with Locust
Section 19: Object Detection with YOLO algorithm
Lecture 87 Understanding object detection
Lecture 88 YOLO paper
Lecture 89 Dataset Preparation
Lecture 90 YOLO Resnet
Lecture 91 Data augmentation
Lecture 92 Testing
Lecture 93 Data generators
Section 20: Image segmentation
Lecture 94 Image Segmentation - Oxford IIIT Pet Dataset
Lecture 95 UNET Model
Lecture 96 Training and optimization
Lecture 97 Data augmentation and dropout
Lecture 98 Class weighting
Section 21: People counting
Lecture 99 People Counting - Shangai Tech Dataset
Lecture 100 Dataset preparation
Lecture 101 CSRNET
Lecture 102 Training and optimization
Lecture 103 Data augmentation
Section 22: Image generation
Lecture 104 Introduction to image generation
Lecture 105 Understanding variational autoencoders
Lecture 106 VAE training and digit generation
Lecture 107 Latent space visualizations
Lecture 108 How GANs work
Lecture 109 Improving GAN training
Lecture 110 Face generation with GANs
Section 23: Text Preprocessing for Sentiment analysis
Lecture 111 Understanding sentiment analysis
Lecture 112 Text standardization
Lecture 113 Tokenization
Lecture 114 One-hot encoding and Bag of Words
Lecture 115 Term frequency - Inverse Document frequency (TF-IDF)
Lecture 116 Embeddings
Section 24: Sentiment Analysis with Recurrent neural networks
Lecture 117 How Recurrent neural networks work
Lecture 118 Data preparation
Lecture 119 Building and training RNNs
Lecture 120 Advanced RNNs (LSTM and GRU)
Lecture 121 1D Convolutional Neural Network
Section 25: Sentiment Analysis with transfer learning
Lecture 122 Understanding Word2vec
Lecture 123 Integrating pretrained Word2vec embeddings
Lecture 124 Testing
Lecture 125 Visualizing embeddings
Section 26: Neural Machine Translation with Recurrent Neural Networks
Lecture 126 Understanding Machine Translation
Lecture 127 Data preparation
Lecture 128 Building, training and testing Model
Lecture 129 Understanding BLEU Score
Lecture 130 Coding BLEU score from scratch
Section 27: Neural Machine Translation with Attention
Lecture 131 Understanding Bahdanau Attention
Lecture 132 Building, training and testing Bahdanau Attention
Section 28: Neural Machine Translation with Transformers
Lecture 133 Understanding Transformer Networks
Lecture 134 Building, training and testing Transformers
Lecture 135 Building Transformers with Custom Attention Layer
Lecture 136 Visualizing Attention scores
Section 29: Sentiment Analysis with Transformers
Lecture 137 Sentiment analysis with Transformer encoder
Lecture 138 Sentiment analysis with LSH Attention
Section 30: Transfer Learning and Generalized Language Models
Lecture 139 Understanding Transfer Learning
Lecture 140 Ulmfit
Lecture 141 Gpt
Lecture 142 Bert
Lecture 143 Albert
Lecture 144 Gpt2
Lecture 145 Roberta
Lecture 146 T5
Section 31: Sentiment Analysis with Deberta in Huggingface transformers
Lecture 147 Data Preparation
Lecture 148 Building,training and testing model
Section 32: Intent Classification with Deberta in Huggingface transformers
Lecture 149 Problem Understanding and Data Preparation
Lecture 150 Building,training and testing model
Section 33: Named Entity Relation with Roberta in Huggingface transformers
Lecture 151 Problem Understanding and Data Preparation
Lecture 152 Building,training and testing model
Section 34: Extractive Question Answering with Longformer in Huggingface transformers
Lecture 153 Problem Understanding and Data Preparation
Lecture 154 Building,training and testing model
Section 35: Ecommerce search engine with Sentence transformers
Lecture 155 Problem Understanding and Sentence Embeddings
Lecture 156 Dataset preparation
Lecture 157 Building,training and testing model
Section 36: Lyrics Generator with GPT2 in Huggingface transformers
Lecture 158 Problem Understanding and Data Preparation
Lecture 159 Building,training and testing model
Section 37: Grammatical Error Correction with T5 in Huggingface transformers
Lecture 160 Problem Understanding and Data Preparation
Lecture 161 Building,training and testing model
Section 38: Elon Musk Bot with BlenderBot in Huggingface transformers
Lecture 162 Problem Understanding and Data Preparation
Lecture 163 Building,training and testing model
Section 39: [DEPRECATED] Introduction
Lecture 164 Welcome
Lecture 165 General Introduction
Lecture 166 Applications of Deep Learning
Lecture 167 About this Course
Lecture 168 Link to Code
Section 40: Essential Python Programming
Lecture 169 Python Installation
Lecture 170 Variables and Basic Operators
Lecture 171 Conditional Statements
Lecture 172 Loops
Lecture 173 Methods
Lecture 174 Objects and Classes
Lecture 175 Operator Overloading
Lecture 176 Method Types
Lecture 177 Inheritance
Lecture 178 Encapsulation
Lecture 179 Polymorphism
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